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Universal Basic Income: The Foreseeable Future of Social Welfare Systems in the Post-Pandemic Era

2023· article· en· W4386641032 on OpenAlexaff
Haoni Yang

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsBasic incomePovertyBasic needsWelfarePandemicDevelopment economicsEconomicsPublic economicsContext (archaeology)PopulationIncome SupportEconomic growthSocial policySocial WelfarePaymentPolitical scienceCoronavirus disease 2019 (COVID-19)SociologyMarket economyMacroeconomicsGeographyFinance

Abstract

fetched live from OpenAlex

The unconditional and regular payment of a certain amount of cash income to the entire population is a claim to universal basic income and a system that is distinct from the existing social welfare systems. Policy experiments on universal basic income have been going on since the end of the 20th century and have been seen by some scholars as a solution to major social problems such as technological development, economic downturn, and poverty. Especially under the impact of the 2020 pandemic, the world's development is once again hampered, and how to better solve the problems of low-income people is an important issue to be addressed by the social welfare system. It is worth noting that the policies introduced by governments under the pandemic have some degree of universal basic income characteristics. This article will analyze the merits and importance of universal basic income in the context of the welfare policies introduced by governments under the pandemic. Considering the economic difficulties in the post-pandemic era, it is worthwhile for countries to explore universal basic income as a basis for new social welfare systems, despite the potential difficulties in implementation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0050.012
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.254
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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